3-D air pollution transmission path identification

ABSTRACT

A system, method and computer program product for tracking and identifying a polluted air mass&#39;s transmission trajectory in real 3-D space. In one aspect, a polluted air mass&#39;s transmission path identification is based on a monitoring of PM2.5 concentration in cubic volumes of an air mass. The method computes a transmission path of polluted air that considers wind-pressure conversion, the displacement estimation with mass concentration, and planetary boundary layer (PBLP height constraint) for 3-D cubic grids. The resultant determination of a polluted air mass&#39;s transmission trajectory in real 3-D space generates more practical and reliable results for intensive knowledge of the transport pathways and potential pollution sources in real 3-D space.

FIELD

The present application relates generally to systems and methods formonitoring air pollution, and more particularly, to methods,apparatuses, and systems for performing 3D-air pollution transmissionpath identification with 3-D pollution monitoring data.

BACKGROUND

Air pollution, e.g., in the form of a heavy haze, often appear in largeareas of a city or country. Current work in monitoring such pollution isbased on 2-D observed pollution data from ground monitoring sites.

A certain type of air pollution includes presence of particulate matter(PM) of various sizes. Fine particular matter, e.g., particles less than2.5 μm (micrometers) in diameter is referred to as PM2.5.

Based on 2-D observed pollution data from ground monitoring sites, thetransport pathways of PM25 can be identified by methods such aspollution correlation analysis.

Currently, a 2D-air pollution transmission path may be identified basedon observed pollution data in monitored sites. For example, 2D-airpollution transmission path can be identified by a backward trajectorymethod.

Other current techniques for investigating the transport pathways andpotential sources of PM25 based on monitoring data uses three methods:backward trajectory cluster analysis, trajectory sector analysis (TSA)and potential source contribution function (PSCF).

SUMMARY

A system, method and computer program product for identifying a 3-D airpollution transmission path based on 3-D pollution monitoring data.

In one aspect, there a computer-implemented method for determining amain transmission path of a polluted air mass comprises: determining, bya processor, a current 3-D contour of pollution at an initial locationand an initial time instant, the current 3-D contour having one or morecubic volumes of polluted air based on measured particulateconcentration levels of an air mass at the initial location; computing,by the processor, based on received wind field condition data, anestimated 3-D contour of pollution for a next time instant and at a nextdestination location, the next destination determined based on estimatedtransport destinations of one or more cubic volumes of polluted air ofthe current 3-D contour from the initial location due to a wind fieldcondition; determining, by the processor, a next current 3-D contour ofpollution at the next destination location and at the next time instant,the next current 3-D contour having one or more cubic volumes ofpolluted air based on new measured particulate concentration levels ofan air mass at the next destination location; and determining, by theprocessor, a transport path of the polluted air mass from the initiallocation to the next destination location at the next time instant, thedetermining based on a degree of transport weight overlap between cubicvolumes of polluted air corresponding to the estimated 3-D contour ofpollution at the next destination location and cubic volumes of pollutedair corresponding to the determined current 3-D contour at the next timeinstant.

Other embodiments include a computer program product and a system.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing aspects and other features are explained in the followingdescription, taken in connection with the accompanying drawings,wherein:

FIG. 1 is a flowchart illustrating a first exemplary method foridentifying a 3D air pollution path in accordance with one or moreembodiments of the present invention;

FIG. 2 conceptually illustrates an example 3-D contour identificationand centroid extraction process 200.

FIG. 3 depicts a defined air mass volume showing the results ofprocessing method according to FIG. 2 for each height layers of apolluted air mass contour;

FIG. 4 depicts a chart showing example pollution areas (locations) atthe various height levels, h1, h2, . . . , hn within a polluted airmass;

FIG. 5 shows an example decomposition of a cubic grid with massconcentration from the polluted air mass in which its displacement canbe estimated in one embodiment;

FIG. 6 show a method for determining the main air pollution transmissionpath using both estimated and observed destinations in one embodiment;

FIG. 7 conceptually shows a determining of a degree of overlap betweenan observed with the estimated in determining a main transport path ofthe polluted air mass between successive time instances;

FIG. 8 shows in one embodiment a table data structure storing thecomputed mass centroids obtained from the processes shown in FIG. 6;

FIG. 9 shows an example air density table showing example airtemperature and pressure conditions and corresponding air densitycomputations; and

FIG. 10 illustrates an exemplary computing apparatus for determining amain transport path of the polluted air mass in accordance with theembodiments of the present invention.

DETAILED DESCRIPTION

A system and method provides for the identification and detection ofpatterns for air pollution moving under 3-D meteorological conditions.Using the system/methods herein, one can find a main potential pollutionsource and transport pathways so that the potential source, likeindustrial factory or traffic ways, can be optimally controlled.

FIG. 1 is a flowchart illustrating a first exemplary method 100 foridentifying a 3D-air pollution transmission path using 3-D pollutionmonitoring data.

In one embodiment, a first method step 105 includes obtaining, at afirst or current time instant, data representing observed locations ofparticulate matter (PM) in a 3-dimensional (3-D) grid of cubic volumesof an air mass. In one embodiment, particulate matter may include PM2.5.Monitoring of these particle concentration levels in the 3-D volume maybe obtained using, for example, UAV (Unmanned Aerial Vehicle) havingsensors that can monitor a volume of air in three dimensions to obtain aPM2.5 concentration data. Further, vehicle-mounted laser radar may beimplemented to detect in a vertical dimension, a PM2.5 distribution indifferent layers of heights. Using the obtained 3-D PM2.5-pollutionmonitoring data, a 3-D cubic grid data of PM2.5 observed concentrationis obtained, e.g., by interpolation.

With the received 3-D monitoring data, intensive knowledge of thetransport pathways and potential sources in real 3-D space of PM2.5 isused to effectively control PM2.5 pollution. After obtaining data fromsensors representing observed locations of particulate matter in the 3-Dvolume of the atmosphere, the system determines, using a programmedprocessor, a concentration value of particles in the 3-dimension volumeof the atmosphere. Then, based on the determined concentrations valuesof particles at locations in the 3-D space, the system identifies, at110, a 3-D “space” contour of air pollution within a 3-dimension volumeof the air mass. In one embodiment, as depicted, several 3-D air masscontours having cubic volumes of polluted air may be identified at afirst time instant (e.g., observed-T1 time) (i.e., a time instant T(N)).

In one aspect, as will be described, each 3-D pollution air masscontour(s) can be recognized (observed particulate concentration) atperiod time intervals, e.g., on the hour, during a heavy pollutionprocess based on 3-D pollution monitoring data.

Continuing to 115, the system further performs identifying based on the3-D space contour of air pollution, a location of a centroid of the airmass. That is, method steps are employed at this step for selecting andcalculating key features to evaluate the polluted air mass, like centerrelative location, length/width/height ratio, concentration distributionand its variation rate with distance.

Continuing to 120, for each of the current observed air mass contour(s),the system further performs estimating a transport of the air pollutionmass by integration of cubic grid displacement with Lagrangian methodand Bernoulli equation (wind-pressure relationship) in a 3-Dmeteorological condition. In one embodiment, the present methods providefor air transport transmission identification of a 3-D pollution contourin consideration of: 1) wind-pressure conversion, 2) displacementestimation with mass concentration, and 3) PBL height constraint for a3-D grid. By accounting for these considerations, the system generatesmore practical and reliable results for intensive knowledge of thetransport pathways and potential sources in real 3-D space.

For each of the observed-T(N) time 3-D pollution contour(s), and basedon the integration of cubic grid displacements for a time interval Δt,the system then determines a corresponding time T(N+1)-estimated 3-Dcontour (estimated air mass destination grids) of polluted air at anestimated transport destination after a transport time Δt.

Finally at 125, the system further implements methods for determining afinal destination and contour of the air pollution after transport. Inone embodiment, as described herein with respect to FIG. 6, the finaldestination and contour of the polluted air mass may be determined witha transport weight by overlap degree between estimated air mass'sdestination grids and observed air mass grids. That is, for example, ifseveral T1-observed masses transport and then merge into one T2-observedair mass, the transport weight of each path may be introduced fromgird-overlap ratio to select the main path.

In one embodiment, the main 3-D air pollution transmission path can beidentified by connecting the 3-D air mass's centroids at eachtransported location during successive time-instances. The 3-D airpollution transmission path identification with 3-D pollution data ismore accurate by consideration of wind-pressure conversion, thedisplacement estimation with mass concentration, and PBL heightconstraint for 3-D grid.

FIG. 2 conceptually illustrates an example of a 3-D contouridentification and centroid extraction process 200.

At a first step 205, a layer of height within a 3D cubic volume airmass, as constrained within a Plantary Boundary Layer (PBL) height(e.g., 300 m˜1000 m), is selected. Then, at 210, the system methodsperform a pollution grid extraction wherein, at a selected location(e.g., latitude and longitude coordinate) and a selected height (“h”),3-D pollution grid(s) is(are) selected. In one embodiment, thedetermining of a pollution grid to select is by the observing of aconcentration of the particulate matter at that height, e.g., asobtained from the UAV sensors, and determining that a concentration ofparticulate matter, e.g., PM2.5 in micrograms per cubic meter (e.g.,μg/m³), at that particular height is greater than a thresholdconcentration, i.e.,PM2.5(lat,lon,height)>Thresh

Where lat and long is a coordinate of a specific location, height is theselected height h, and Thresh is a threshold particulate concentrationvalue above which will assign a grid area at this height. This PM2.5concentration value (as a function of lat, lon, value) is based on a 3-Dcubic grid data of PM2.5 observed concentration.

At 215, a step is determined whether, for that current layer, there areany further pollution grids identified. If there are further identifiedpollution grids that can be identified in the current height layer, thenthe process returns back to 210 to select a next pollution grid at thatcurrent layer. If, at 215, it is determined that there are no furtheridentified pollution grids that can be identified in the current heightlayer, then the process proceeds to 220, where a determination is madeto identify and record the pollution “areas” in each height layer.

At 220, to determine or identify the pollution areas in a layer, adetermination is made as to whether a distance between two pollutiongrids is less than a threshold distance.

In one embodiment, the identifying of a pollution area in a height layeris to determined whetherDist(Δlat,Δlon)<xwherein Dist( ) is a distance between any two coordinate locations,i.e., a Δlat, Δlon, in which particulate matter has been found in aheight layer, and “x” is a threshold distance within which the two gridsare regarded in the same “pollution area.”

Then, continuing at 225, for all pollutions areas at that layer, adetermination is made as to whether the number of pollution areas isgreater than a threshold number of area, i.e., whether:a number of pollution areas >thresh

If, at 225, it is determined that the number of identified pollutionareas at that height is not greater than a threshold number of areas,then, at 240, a determination is made as to whether there are any moreheight layers to process. If there are no more height layers to process,then the process ends. Otherwise, at 240, if it is determined that thereare further height layers to process, then the next current heightlayer, e.g., a height layer h+1, is obtained at 245, and processingre-commences by returning back to step 205, to repeat running theprocessing steps 210-240 again at a next current height layer.

Otherwise, at 225, if it is determined that the number of identifiedpollution areas at that height is greater than a threshold number ofareas, i.e., number of pollution areas >thresh, then, at 230, therespective areas can be recorded as Area1_h, Area2_h, . . . , etc.,where h is the current height layer. Then, then, at 240, a determinationis made as to whether there are any more height layers to process. Ifthere are no more height layers to process, then the method ends.Otherwise, at 240, if it is determined that there are further heightlayers to process, then the next current height layer, e.g., a heightlayer h+1, is obtained at 245, and processing re-commences by returningback to step 205, to repeat running the processing steps 210-225 againat a next current height layer, e.g., height layer h+1.

FIG. 3 shows a defined air mass 300 showing the results of processingmethod according to FIG. 2 for each of example height layers 305, 310,320, etc. As shown, a height limit 350 is identified that represents aplanetary boundary layer (PBL) height constraint.

FIG. 3 further shows the determined pollution (cubic) grids 312, 314,316 where above-threshold concentrations of particulate matter, e.g.,PM2.5, have been found, e.g., above and below example height layer 310as shown. An accompanying legend 360 shows an embodiment of a colormapping scheme for mapping a color on the legend corresponding to aparticulate matter concentration in a height layer of the air mass gridsuch that the number of pollution areas is determined at differentlocations (areas) within a height layer. In one embodiment, methods areimplemented herein that identify time-series masses in a same cluster(e.g., color) to generate 3-D air pollution transmission path. Forexample, these pollution grids 312, 314 and 316 are 3-dimensional gridsobtained from observed areas of pollution, e.g., areas Area1_h andArea2_h, by the ground-based and/or UAV-based pollution monitoringsensors. A 3-D contour of air pollution within the 3-D grid definingvolume 300 is identified based on all the observed areas at differentlocations within all height layers. From the identified pollution(cubic) grids 312, 314, 316 having above-threshold concentrations ofparticulate matter, there may be identified a 3D-contour of pollution.

FIG. 4 depicts a chart 400 showing recorded examples of pollution areas(locations) at the various height levels, h1, h2, . . . , hn within theair mass 300. For example, at a first height h1 405, there is depictedan area Area1_h1 410 having particulate matter found therein in amountsthat exceed the set threshold and thus contribute to the air masscentroid. Additional areas at height level h1, i.e., Area2_h1, AreaM_h1do not have particulate matter found therein in amounts that exceed theset threshold. Chart 400 may be completed after repeating method 200 ofFIG. 2 to show, at each height level h2, . . . , hn, and correspondingareas therein having particulate matter (e.g., PM2.5) concentrationsexceeding threshold amounts. For example, at height h2, a first area,Area1_h2, does not have particulate matter concentration exceedingthreshold amount but a second area Area2_h2 415, does have particulatematter concentration exceeding threshold amount. Similarly, at theheight hn, a first area Area1_hn 420 does have particulate matterconcentration exceeding threshold amount. There overlapping areas of theidentified pollution (cubic) grids having above-threshold concentrationsof particulate matter are used to identify a 3D-contour of pollution.

In one embodiment, the processing performed at each height layer ofFIGS. 2, 3 is performed at a first time instance, e.g., T1. In oneembodiment, further processing includes invoking methods to determinethe observed 3-D polluted air mass at a time T1, i.e., a Mass1_T1, byintegrating the overlapped pollution area(s) in adjacent layers ofheight. In view of the chart found in FIG. 4, there is determined a masscentroid of the air mass at time T1, i.e., a value referred to asMass1_T1, as the integration of the areas Area1, . . . , AreaM inheights h, . . . , hn having particulate matter concentrationsdetermined to exceed the threshold concentration amount as shown in FIG.3. In the example of FIG. 4, PM concentrations in areas 410, 415, 420etc. will be integrated over the entire air mass 300 and a centroid ofthe polluted air mass determined at time instance T1, i.e., Mass1_T1.

Returning back to FIG. 1, step 115 performs the identifying of alocation of a centroid of the air mass at the current time instant,e.g., T1. More particularly, the centroid location, r_(σ), of a 3-Dpolluted air mass 300 may be calculated by the quality m of each cubicgrid (=PM2.5 concentration in a grid*grid volume) defining the contouraccording to equation 1) as follows:

$\begin{matrix}{{r_{\sigma} = \frac{\sum\limits_{i}{m_{i}r_{i}}}{M}},} & \left. 1 \right)\end{matrix}$where, M is the total mass of the polluted air mass, and for each cubicgrid i, the concentration (μg/m³) and the unit mass m_(i) are obtained(i.e., according to concentration*grid volume). By selecting one fixedpoint, the distance r_(i) between a pollution grid i with the fixedpoint is calculated, and then the centroid location, r_(σ) is obtained.

Returning back to FIG. 1, step 120 performs the estimating of atransport path or transmission path of the polluted air mass byintegration of cubic grid displacement with Lagrangian method andBernoulli equation (wind-pressure relationship) in a 3-D meteorologiccondition.

As “wind” speed is one factor to make the air mass with suspendedparticles diffuse, it is assumed that the displacement of the particlesis determined by a wind field. Then the location of the particle'strajectory can be obtained by implementing time-and-space integralcalculus. In one embodiment, in a simplified form of the Lagrangiandiffusion method, the location of the particle's trajectory is computedfrom an average of three-dimensional velocity vectors at aninitial-position P(t) and a first-guess position P′(t+Δt) according to:P′(t+Δt)=P(t)+V(P,t)Δtwhere V is a velocity vector (wind speed). In one embodiment, thevelocity vectors are linearly interpolated in both space and time. Theintegration time step (Δt) can vary during simulations performed at thecomputing system of FIG. 10. In one embodiment the time step is computedfrom the requirement that an advection distance per time-step should beless than a grid spacing.

The displacement of each cubic grid in T1-observed polluted air mass toa T2 time is estimated with computer processes applying a Lagrangianmethod and the Bernoulli equation that describes a wind-pressurerelationship. In one embodiment, the wind speed to the cubic grid in 3-Dspace is calculated by synthesis of a horizontal and vertical windvector, such as may be obtained from NCEP FNL (Final) Operational GlobalAnalysis data (managed by the Data Support Section of the Computationaland Information Systems Laboratory at the National Center forAtmospheric Research in Boulder, Colo.) or like numerical weatherprediction model (e.g. Weather Research and Forecasting (WRF) Model).

FIG. 5 depicts an example decomposition 500 of an example PM2.5 pollutedcubic grid 505 with mass concentration from the polluted air mass inwhich its displacement can be estimated with its initial velocity andthe accelerated velocity by dynamic pressure from 3-D wind. That is,using cubic grid decomposition, a displacement estimation of cubic grid505 along horizontal and vertical directions can be found according tothe obtained wind field from a numerical model, e.g., represented by acombined wind vector V 515 (e.g., in m/s) having horizontal wind forcecomponent vector 510 and vertical wind force component 520, and the massconcentration of the grid.

According to wind-pressure relationship set forth in the Bernoulliequation, the dynamic pressure from 3-D wind is governed according toequation 2) as follows:wp=0.5·ρ·V ²  2)where wp is wind-pressure (e.g., in kN/m²), ρ is the air (mass) density(e.g., in kg/m³), and V is the wind speed (e.g., in m/s). The airdensity is obtained from available meteorological temperature andpressure data for that location/time according to the followingequation:air density=1.293*(Pressure/1atm)*(273.15/absolute temperature)where absolute temperature=degree Celsius+273.15. FIG. 9 shows anexample air density table 900 showing example air temperature andpressure conditions and corresponding air density computations asavailable from on-line sources. Continuing with the processing at step120, there is computed the Δt-displacement according to equation 3) asfollows:ΔS=v0 Δt+½aΔt ²  3)where the accelerated velocity a=wp*S/(C*Vgrid), C is the PM2.5concentration in this grid, S is the stressed area, and Vgrid is thecubic grid's volume.

Then, a transport of the air pollution from T1 to T2 time is estimatedby integration of cubic grid displacement. In one embodiment, theT2-estimated air mass, e.g., Mass2_T1, and particularly the T2-estimatedgrids are kept in continuous air mass. Any dispersed T2-estimated gridsare removed from the air mass. Additionally removed are the T2-estimatedgrids estimated to be transported above the PBL height when thecorresponding T1-observed grids are under the PBL height. In oneembodiment, these T1-observed grids below the PBL height can not reachthe PBL height due to the constraint of vertical diffusion of particleswithin local PBL height.

FIG. 6 show a method for determining the main air pollution transmissionpath using both estimated and observed destinations such as described atstep 125, FIG. 1. FIG. 6 depicts an iterative method, in which the main(polluted air mass) transporting locations in times T1˜Tn can bedetermined successively.

First, at 605, FIG. 6, there is performed the observing and identifyingone or more current 3-D contour(s) of pollution at a current locationand extracting a mass centroid from each, e.g., Mass1_T(N), Mass2_T(N),. . . , Massm_T(N), based on 3-D cubic grid data of PM2.5 observedconcentration in a current (next) time instance after transport timeinterval Δt. Steps 105-120 of FIG. 1 may be used to perform this.Additionally obtained are the prior corresponding estimated contour pathdestinations for each current 3-D contour computed for the transporttime interval Δt (T(N)-T(N−1)) based on respective prior timeT(N−1)-observed 3-D contour(s) (e.g., Mass1_T(N−1), Mass2_T(N−1), . . ., Massm_T(N−1)). Then, at 607, a comparison is made of a currentT(N)-observed 3D contour, e.g., Mass1_T(N), with a time T(N−1)-estimated3-D contour, e.g., Mass2_T(N−1), and, at 610, a determination is made asto a degree of grid overlap between the current observed Mass1_T(N) withone of the prior estimation transported air mass 3-D contours, e.g.,Mass2_T(N−1), which degree of overlap is used to determine a strength ofthe transport path between successive time instants.

Then continuing to step 615, FIG. 6, a determination is made as towhether Mass1_T(N) is highly overlapped with T2-estimated air mass fromMass2_T(N−1) after computing transport time Δt. For example, at 605,there may be obtained a time T2-observed current 3-D contour (of actualdisplaced or transported polluted cubic volumes) and obtainedcorresponding mass centroid at T2, i.e., Mass1_T2. At 610, this may becompared to the T2 -estimated 3-D contour based on a determinedtransport time Δt (and based on recorded wind and meteorologicalcondition data), and having a computed mass centroid, e.g., Mass2_T1. Asshown in FIG. 7, the degree of overlap is computed to ascertain astrength of these potential transport paths for the air mass between thetime instants T1-T2 (=Δt).

In one embodiment, the Δt=T2−T1 may be set as 10.0 minutes, or any othertime interval. For example, the Δt may be the conventional time intervalof meteorologic and pollution data, e.g., 1 hour. In one embodiment,data can be interpolated to a shorter time step of 10 min. for higherresolution calculations.

FIG. 7 conceptually shows the determination of a degree of overlap 700between the current observed polluted air mass Mass1_T2 715 at time T2(=time T(N)) with one of the estimated contours from the prior timeinstant, e.g., Mass1_T1 705, Mass2_T1 710, etc. at time T1 (=timeT(N−1)), in determining a main transport path for the polluted air mass.For example, based on the degree of overlap, either a transport path,e.g., PathT1_1 712 corresponding to a transport of the polluted air massT1-observed Mass1_T1 705 or a path PathT1_2 722 corresponding to atransport of the polluted air mass T1-observed Mass2_T1 710 is selectedas the transport path for the air mass between time instances T1-T2.

Whether the time T2-observed air mass value, i.e., Mass1_T2 715, ishighly overlapped with a T2-estimated air mass, e.g., Mass1_T1 705,Mass2_T1 710, etc. after the computed transport time Δt=T2−T1 isdetermined using the processes leading up to and including equations1)-3) for estimating air mass transport by integration of cubic griddisplacement. As shown in FIG. 7, as an example, the current T2-observedair mass value Mass1_T2 715 is determined more highly overlapped withT2-estimated air mass 711 from Mass2_T1 710 after Δt=T2−T1 transport(transport estimation), as would be indicated by a higher computedgrid-overlap ratio (X2%) 725. In comparison, the grid-overlap withT2-estimated air mass 706 from Mass1_T1 705 after transport time Δtindicates a lower computed grid-overlap ratio (X1%) 723.

Thus, returning to 615, FIG. 6, based on the results of the comparisonbetween estimated and observed 3-D polluted air mass contours, anassociated weight will be assigned to the respective path based on thedetermined degree of overlap.

In the example of FIG. 7, with the higher grid-overlap ratio determinedwith these estimated and observed 3-D contours, it is regarded thatT2-observed air mass Mass1_T2 715 is partly generated by the transportof T1-observed air mass, i.e., Mass2_T1 710, and the method at 620, FIG.6 assigns a larger transport weight to this transport path 722 betweentime instant T1 and T2. That is, the greater degree of overlap, then theless difference there is between the averaged original PM2.5 value anddestination value, and the more weight for this associated path.

Otherwise, if at 615, FIG. 6 the new T2-observed air mass Mass1_T2 715is overlapped less with T2-estimated air mass 706 from Mass1_T1 705after Δt=T2−T1 transport (transport estimation), as shown in FIG. 7, itis regarded that T2-observed air mass Mass1_T2 715 is not partlygenerated by the transport of T1-observed air mass, i.e., Mass1_T1 705,and the method at 625, FIG. 6 assigns a smaller transport weight to thistransport path 712 between time instant T1 and T2. That is, the lessdegree of overlap, then the greater the difference there is between theaveraged original PM2.5 value and destination value, and the less weightassigned for this path.

In one embodiment, a transport weight for a path, e.g., a PathT1_X (attime T1 or any time instant such as shown in FIG. 7) may be computedaccording to the following expression:Transport weight of PathT1_X=w1*gird-overlapratio+w2/abs(avg(PM2.5inOverlapPart)−avg(PM2.5inMassX_T1))where X=1, 2, . . . , m, for time T1, and w1 and w2 (>0) arecoefficients for the two factors.

Continuing to step 630, FIG. 6, a determination is made as to whetherthere are any more transport paths to compare current time T(N)-observed3-D contours to compare grid-overlap between corresponding timeT(N−1)-estimated 3-D contours for the transport time Δt. For example, itmay be the case that several time T1-observed masses transport and thenmerge into one time T2-observed air mass. In this embodiment, the methodof FIG. 6 generates a transport weight for each air mass based onrespective computed grid-overlap ratios, and based on the ratios,selects a main transport path.

Thus, at 630, if there are more transport paths to compare, then themethod returns to step 607, FIG. 6, and repeats all steps 607-630 untilno more current time T(N)-observed 3-D contours remain to compare. Atsuch time, the method proceeds to step 635 to select transport pathdestination of the polluted air mass based on estimated contour havinggreatest grid-overlap degree with the current contour and record thecurrent observed 3-D contour centroid and path.

Continuing to 640, FIG. 6, a determination is made as to whether thecurrent observed 3-D contour and computed centroid at timeT(N)<threshold particulate matter concentration level. If the currentobserved 3-D contour and computed centroid at time T(N) still has PM2.5concentrations exceeding acceptable threshold level, then the processrepeats to determine the next destination of the transport path of airmass 300. At 645, the time instant N is incremented (i.e., N=N+1) andthe process is repeated for a next transport time interval Δt byreturning to step 605. Then, all steps of FIG. 6 are repeated todetermine the next transport path (transport destination) based on thenext T(N+1)-observed 3D contour, e.g., Mass1_T(N+1). For the example,this next observed 3D contour will be determined at the Δt=T3−T2transport interval having a centroid Mass1_T3 and associated estimatedtransport path PathT2_1 732. The iterative process steps 605-645 iscontinued until a final transport path 742 is determined for an observed3-D pollution contour having a centroid mass Mass1_Tn.

In one embodiment, the iterative process steps 605-645 is continueduntil at 640, it is determined that the current observed concentrationin the transported polluted air mass is less than an acceptablethreshold PM2.5 concentration level, e.g., corresponding to acceptablequality air. At such time, the process will proceed to step 650 torecord the location of the current T(N)-observed 3D contour as the finaltransport destination location of the original polluted air mass and theprocess ends.

As a result of the processing of FIG. 6, the system has generated andrecorded in a memory device, the computed mass centroid values computedfor each one or more 3-D contours observed at each time instant, T=T1,T2, . . . , Tn (e.g., N=1, 2, . . . , n) and their correspondingcoordinate locations, e.g., a latitude and longitude. That is, for alltime instants T(N), there is recorded all computed mass centroids andtheir locations for all observed 3-D contours.

FIG. 8 shows in one embodiment, a table data structure 800 storing thecomputed centroids, e.g., Mass1_T1, Mass2_T1, . . . , Massm_T1 for timeT1; Mass1_T2, Mass2_T2, etc. for time T2, etc. until the last iterationwhich records Mass1_Tn, Mass2_Tn, etc. for time Tn. The sequencedepicted by successively selected contours having determined masscentroids Mass2_T1 805, Mass1_T2, 810 and Mass1_Tn 829 represent thetransport path destinations of the polluted air mass at each successivetime interval Δt.

Thus, by the iterative process of FIG. 6, the main transportinglocations at successive times T1˜Tn can be determined by the successiveobserved 3-D contours having greatest degree of grid-overlap. The main3-D air pollution transmission path can be obtained by connecting the3-D air mass's centroids during its time-series destination locations asshown by connected centroids in FIG. 8. In one embodiment, thedetermination of the “Main” transport path is obtained by integratingeach step path with maximum transport weight, as follows:Main Path=Max(Transport weight of PathT1_X)->Max(Transport weight ofPathT2_X-> . . . ->Max(Transport weight of Path Tn_X)

In one embodiment, the system, methods and iterative processes describedherein may be used to provide dynamic control of polluting sources. Forexample, by continually recording various meteorological and windcondition data over time, a computational model may be generated thatrelates various historical wind field, meteorological conditions and PLBheight constraint data affecting displacement of masses of polluted air,and particularly, their transport or transmission path(s). Then, givenhistorical wind/meteorological conditions correlated with maintransmission paths of polluted air masses, a transport of a currentpolluted air mass at an initial location may be predicted. From thisprediction, a source(s) of pollution that provide the concentrationlevels of particulates observed in the air mass at the initial locationcan be dynamically controlled.

That is, a given input of real-time current or expected wind fieldcondition data, meteorological condition data and PLB constraintcondition at an initial location may be compared with past historicalscenarios of like wind and meteorological conditions for which arespective historical transmission path(s) of polluted air mass(es)between initial and final locations has(have) been already determined.That is, using the computational model, based on the comparing of thecurrent input real-time wind condition data against the historic windcondition data and given any current particulate matter concentrationslevels, signals may be automatically generated to trigger a pollutionsource to increase or decrease a frequency of particulate emission orparticulate generating, or control an amount of particulates generatedat the respective pollution source(s) in order to preserve or maintain aparticulate concentration level at a various location(s) to within aspecified level. These steps may be performed to regulate air quality atanywhere along a transport path corresponding to a similar historictransmission path for the similar historical wind field andmeteorological conditions.

FIG. 10 illustrates an example computing system in accordance with thepresent invention. It is to be understood that the computer systemdepicted is only one example of a suitable processing system and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the present invention. For example, thesystem shown may be operational with numerous other general-purpose orspecial-purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the system shown inFIG. 10 may include, but are not limited to, personal computer systems,server computer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

In some embodiments, the computer system may be described in the generalcontext of computer system executable instructions, embodied as programmodules stored in memory 16, being executed by the computer system.Generally, program modules may include routines, programs, objects,components, logic, data structures, and so on that perform particulartasks and/or implement particular input data and/or data types inaccordance with the present invention (see e.g., FIG. 2).

The components of the computer system may include, but are not limitedto, one or more processors or processing units 12, a memory 16, and abus 14 that operably couples various system components, including memory16 to processor 12. In some embodiments, the processor 12 may executeone or more modules 10 that are loaded from memory 16, where the programmodule(s) embody software (program instructions) that cause theprocessor to perform one or more method embodiments of the presentinvention. In some embodiments, module 10 may be programmed into theintegrated circuits of the processor 12, loaded from memory 16, storagedevice 18, network 24 and/or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

The computer system may include a variety of computer system readablemedia. Such media may be any available media that is accessible bycomputer system, and it may include both volatile and non-volatilemedia, removable and non-removable media.

Memory 16 (sometimes referred to as system memory) can include computerreadable media in the form of volatile memory, such as random accessmemory (RAM), cache memory an/or other forms. Computer system mayfurther include other removable/non-removable, volatile/non-volatilecomputer system storage media. By way of example only, storage system 18can be provided for reading from and writing to a non-removable,non-volatile magnetic media (e.g., a “hard drive”). Although not shown,a magnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 14 by one or more datamedia interfaces.

The computer system may also communicate with one or more externaldevices 26 such as a keyboard, a pointing device, a display 28, etc.;one or more devices that enable a user to interact with the computersystem; and/or any devices (e.g., network card, modem, etc.) that enablethe computer system to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces20.

Still yet, the computer system can communicate with one or more networks24 such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter22. As depicted, network adapter 22 communicates with the othercomponents of computer system via bus 14. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with the computer system. Examples include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allelements in the claims below are intended to include any structure,material, or act for performing the function in combination with otherclaimed elements as specifically claimed. The description of the presentinvention has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to theinvention in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope and spirit of the invention. The embodiment was chosen anddescribed in order to best explain the principles of the invention andthe practical application, and to enable others of ordinary skill in theart to understand the invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A system for determining a main transmission pathof a polluted air mass comprising: one or more sensors for measuring aconcentration level of particulates in an air mass at one or morelocations; and at least one processor and a memory coupled to the atleast one processor, wherein the memory comprises instructions which,when executed by the at least one processor, cause the at least oneprocessor to: determine a current 3-D contour of pollution at theinitial location and an initial time instant, said current 3-D contourhaving one or more cubic volumes of polluted air based on the measuredparticulate concentration levels of the air mass at said initiallocation; compute, based on a received wind field condition data, anestimated 3-D contour of pollution for a next time instant and at a nextdestination location, said next destination determined based onestimated transport destinations of one or more cubic volumes ofpolluted air of said current 3-D contour from the initial location dueto a wind field condition; determine a next current 3-D contour ofpollution at the next destination location and at said next timeinstant, said next current 3-D contour having one or more cubic volumesof polluted air based on new measured particulate concentration levelsof an air mass at said next destination location; and determine atransport path of said polluted air mass from the initial location tothe next destination location at said next time instant, wherein, todetermine a transport path, said at least one processor computes adegree of transport weight overlap between cubic volumes of polluted aircorresponding to the estimated 3-D contour of pollution at said nextdestination location and cubic volumes of polluted air corresponding tosaid determined current 3-D contour at said next time instant; anditerate, at each of next successive time instances and locations, thecomputing of an estimated 3-D contour of pollution to determine atransport path of said polluted air mass, subject to said wind fieldcondition, at each successive next location, connect each transport pathof said polluted air mass determined at each iteration to determine amain transmission path of said polluted air mass from said initiallocation to a final location; and dynamically control a source ofpollution configured to provide said concentration levels ofparticulates observed in said air mass at said initial location, whereinto dynamically control, said at least one processor is furtherconfigured to: obtain a past main transmission path of a polluted airmass between initial and final locations based on historic wind andmeteorological condition data; compare a current input real-time windcondition data against said similar historic wind condition data; andcontrol an increase or decrease an amount of particulates generated atsaid pollution source based on said comparing.
 2. Thecomputer-implemented system of claim 1, wherein to iterate the computingof an estimated 3-D contour of pollution to determine a transport path,said at least one processor device is further configured to: iterate, ateach time instant of successive time instances: the computing of anestimated 3-D contour of pollution for a next time instant and at a nextdestination location, wherein an initial location of a current iterationcorresponds to a next destination location of an immediate prioriteration; the determining of a next current 3-D contour of pollution atthe next destination location based on measured particulates in said airmass at said next destination location; and the determining of atransport path of said polluted air mass between the initial location tothe next current destination location for the current iteration.
 3. Thecomputer-implemented system according to claim 1, wherein said at leastone processor is further configured to: continually record variousmeteorological and wind condition data over time in the memory; generatea model to correlate various recorded historical wind field andmeteorological conditions data that resulted in displacement of massesof polluted air along connected transport paths forming a maintransmission path between said initial and final locations; and run saidmodel to estimate a transport of a polluted air mass currently at aninitial location.
 4. The computer-implemented system of claim 2, whereinduring said iterating, said at least one processor is further configuredto: determine whether concentration levels of particulates at an airmass at said next current destination location drops below a pre-setthreshold concentration of particulates; and determine a final transportdestination as said next destination location when said concentrationlevels at said next destination location are determined below thepre-set threshold concentration of particulates.
 5. Thecomputer-implemented system according to claim 3, wherein responsive tocomparing a current input real-time wind condition data against saidsimilar historic wind condition data, said at least one processor isfurther configured to: automatically generate a trigger signal totrigger a pollution source to control an amount of particulatesgenerated at the respective pollution source(s) such that a particulateconcentration level is maintained at a location along the transport pathis to within a specified level.
 6. The computer-implemented systemaccording to claim 4, wherein said at least one processor is furtherconfigured to: receive input data comprising locally observedconcentration levels of particulates at different layers of an air massand at different locations within each said layer at a time instant;based on said input data, identify a 3-D contour of pollution in saidair mass at an initial location, said contour having one or more cubicvolumes of pollution based on observed concentration levels ofparticulates exceeding a threshold level at said time instant; identify,based on said received input data, additional 3-D contours of pollutionat respective additional initial locations, each said additional 3-Dcontour having one or more cubic volumes of pollution based on observedconcentration levels of particulates exceeding a threshold level at saidtime instant at each said additional initial locations; and for eachadditional 3-D contour of pollution, based on said received wind fieldcondition data, compute an estimated transport destination of said oneor more cubic volumes of polluted air at said next time instant in eachrespective said additional 3-D contour; and for each additional 3-Dcontour of pollution: compute a corresponding estimated 3-D contour ofpollution at said subsequent time instant, each respective saidcorresponding estimated 3-D contour based on estimated transportdestinations of said one or more cubic volumes of polluted air withineach; and determining a degree of transport weight overlap between cubicvolumes of polluted air corresponding to the estimated 3-D contour ofpollution and cubic volumes of polluted air corresponding to the current3-D contour of pollution at said current destination location and saidnext time instant; and select the transport path of a polluted air massfrom one of said additional initial locations to the current destinationlocation based having a greatest degree of transport weight overlap. 7.The computer-implemented system according to claim 6, wherein said atleast one processor is further configured to: determine a merge of twoor more said additional 3-D contours of pollution that form said current3-D contour of pollution at said current destination location at saidsubsequent time instant; and select a main transport path of a pollutedair mass from one of said additional initial locations based on arespective degree of transport weight overlap of each respectivetransport path with said current 3-D contour of pollution.
 8. A computerprogram product for determining a main transmission path of a pollutedair, the computer program product comprising a computer-readable storagemedium having a computer-readable program stored therein, wherein thecomputer-readable program, when executed on a processor, causes theprocessor to: determine a current 3-D contour of pollution at an initiallocation and an initial time instant, said current 3-D contour havingone or more cubic volumes of polluted air based on measured particulateconcentration levels of an air mass at said initial location; compute,based on received wind field condition data, an estimated 3-D contour ofpollution for a next time instant and at a next destination location,said next destination determined based on estimated transportdestinations of one or more cubic volumes of polluted air of saidcurrent 3-D contour from the initial location due to a wind fieldcondition; determine a next current 3-D contour of pollution at the nextdestination location and at said next time instant, said next current3-D contour having one or more cubic volumes of polluted air based onnew measured particulate concentration levels of an air mass at saidnext destination location; and determine a transport path of saidpolluted air mass from the initial location to the next destinationlocation at said next time instant, wherein, to determine a transportpath, said at least one processor computes a degree of transport weightoverlap between cubic volumes of polluted air corresponding to theestimated 3-D contour of pollution at said next destination location andcubic volumes of polluted air corresponding to said determined current3-D contour at said next time instant; and iterate, at each of nextsuccessive time instances and locations, the computing of an estimated3-D contour of pollution to determine a transport path of said pollutedair mass, subject to said wind field condition, at each successive nextlocation, connect each transport path of said polluted air massdetermined at each iteration to determine a main transmission path ofsaid polluted air mass from said initial location to a final location;and dynamically control a source of pollution configured to provide saidconcentration levels of particulates observed in said air mass at saidinitial location, wherein to dynamically control, said at least oneprocessor is further configured to: obtain a past main transmission pathof a polluted air mass between initial and final locations based onhistoric wind and meteorological condition data; compare a current inputreal-time wind condition data against said similar historic windcondition data; and control an increase or decrease an amount ofparticulates generated at said pollution source based on said comparing.9. The computer program product of claim 8, wherein to iterate thecomputing of an estimated 3-D contour of pollution to determine atransport path, the computer-readable program, when executed on aprocessor, further causes the processor to: iterate, at each timeinstant of successive time instances: the computing of an estimated 3-Dcontour of pollution for a next time instant and at a next destinationlocation, wherein an initial location of a current iteration correspondsto a next destination location of an immediate prior iteration; thedetermining of a next current 3-D contour of pollution at the nextdestination location based on measured particulates in said air mass atsaid next destination location; and the determining of a transport pathof said polluted air mass between the initial location to the nextcurrent destination location for the current iteration.
 10. The computerprogram product of claim 8, wherein said computer-readable program, whenexecuted on a processor, further causes the processor to: determine amerge of two or more said additional 3-D contours of pollution that formsaid current 3-D contour of pollution at said current destinationlocation at said subsequent time instant; and select a main transportpath of a polluted air mass from one of said additional initiallocations based on a respective degree of transport weight overlap ofeach respective transport path with said current 3-D contour ofpollution.
 11. The computer program product of claim 8, wherein saidcomputer-readable program, when executed on a processor, further causesthe processor to: continually record various meteorological and windcondition data over time in the memory; generate a model to correlatevarious recorded historical wind field and meteorological conditionsdata that resulted in displacement of masses of polluted air alongconnected transport paths forming a main transmission path between saidinitial and final locations; and run said model to estimate a transportof a polluted air mass currently at an initial location.
 12. Thecomputer program product of claim 9, wherein said computer-readableprogram, when executed on a processor, further causes the processor to:during said iterating: determine whether concentration levels ofparticulates at an air mass at said next current destination locationdrops below a pre-set threshold concentration of particulates; anddetermine a final transport destination as said next destinationlocation when said concentration levels at said next destinationlocation are determined below the pre-set threshold concentration ofparticulates.
 13. The computer program product of claim 11, wherein toresponsive to comparing a current input real-time wind condition dataagainst said similar historic wind condition data, said saidcomputer-readable program, when executed on a processor, further causesthe processor to: automatically generate a trigger signal to trigger apollution source to control an amount of particulates generated at therespective pollution source(s) such that a particulate concentrationlevel is maintained at a location along the transport path is to withina specified level.
 14. The computer program product of claim 12, whereinsaid computer-readable program, when executed on a processor, furthercauses the processor to: receive input data comprising locally observedconcentration levels of particulates at different layers of an air massand at different locations within each said layer at a time instant;based on said input data, identify a 3-D contour of pollution in saidair mass at an initial location, said contour having one or more cubicvolumes of pollution based on observed concentration levels ofparticulates exceeding a threshold level at said time instant; identify,based on said received input data, additional 3-D contours of pollutionat respective additional initial locations, each said additional 3-Dcontour having one or more cubic volumes of pollution based on observedconcentration levels of particulates exceeding a threshold level at saidtime instant at each said additional initial locations; and for eachadditional 3-D contour of pollution, based on said received wind fieldcondition data, compute an estimated transport destination of said oneor more cubic volumes of polluted air at said next time instant in eachrespective said additional 3-D contour; and for each additional 3-Dcontour of pollution: compute a corresponding estimated 3-D contour ofpollution at said subsequent time instant, each respective saidcorresponding estimated 3-D contour based on estimated transportdestinations of said one or more cubic volumes of polluted air withineach; and determining a degree of transport weight overlap between cubicvolumes of polluted air corresponding to the estimated 3-D contour ofpollution and cubic volumes of polluted air corresponding to the current3-D contour of pollution at said current destination location and saidnext time instant; and select the transport path of a polluted air massfrom one of said additional initial locations to the current destinationlocation based having a greatest degree of transport weight overlap.